Collision cross section measurement and prediction methods in omics

Abstract

Omics studies such as metabolomics, lipidomics, and proteomics have become important for understanding the mechanisms in living organisms. However, the compounds detected are structurally different and contain isomers, with each structure or isomer leading to a different result in terms of the role they play in the cell or tissue in the organism. Therefore, it is important to detect, characterize, and elucidate the structures of these compounds. Liquid chromatography and mass spectrometry have been utilized for decades in the structure elucidation of key compounds. While prediction models of parameters (such as retention time and fragmentation pattern) have also been developed for these separation techniques, they have some limitations. Moreover, ion mobility has become one of the most promising techniques to give a fingerprint to these compounds by determining their collision cross section (CCS) values, which reflect their shape and size. Obtaining accurate CCS enables its use as a filter for potential analyte structures. These CCS values can be measured experimentally using calibrant-independent and calibrant-dependent approaches. Identification of compounds based on experimental CCS values in untargeted analysis typically requires CCS references from standards, which are currently limited and, if available, would require a large amount of time for experimental measurements. Therefore, researchers use theoretical tools to predict CCS values for untargeted and targeted analysis. In this review, an overview of the different methods for the experimental and theoretical estimation of CCS values is given where theoretical prediction tools include computational and machine modeling type approaches. Moreover, the limitations of the current experimental and theoretical approaches and their potential mitigation methods were discussed.

Keywords: CCS; computational methods; ion mobility; machine learning; omics.

Figure 1.

Different Ion mobility technologies with representative illustrations of separation mechanisms, applied field, and gas dynamics.

Figure 2.

PS-IM-MS and PS-MS/MS spectra obtained in the negative ion mode of the 5 Bacillus species where (A) Ion mobility spectra revealed the presence of various lipid isomers of m/z 721.51 (PG (32:0)) and (B) Tandem mass spectra (MS/MS) spectra of m/z 721.51 which support the identification of the lipid isomers by the IM spectra (Adapted with permission from ref. , Copyright (2022) Elsevier)).

Figure 3.

The relative prediction difference between theoretical CCS and experimental CCS for different glucuronide compounds. Orange bars show the difference between the experimental and ML CCS values, while blue bars show the difference between the experimental and QM approaches in CCS values (Adapted with permission from ref. , Copyright (2021) American Chemical Society).

Figure 4.

MRE and RMSE difference between different ML tools for [M-H]⁻ (a+d), [M+H]⁺ (b+e), and [M+Na]⁺ (c+f) (Adapted with permission from ref. , Copyright (2022) American Chemical Society).

Figure 5.

The relationship between experimental CCS vs predicted CCS on regular vs non-regular peptides. (Adapted with permission from ref. , Copyright (2022) Nature Portfolio).